Products that include images are a popular keepsake or gift for many people. Such products typically include an image captured by a digital camera that is inserted into the product and is intended to enhance the product, the presentation of the image, or to provide storage for the image. Examples of such products include picture albums, photo-collages, posters, picture calendars, picture mugs, t-shirts and other textile products, picture ornaments, picture mouse pads, and picture post cards. Products such as picture albums, photo-collages, and picture calendars include multiple images.
When designing or specifying photographic products, it is desirable to select a variety of images that provide interest, aesthetic appeal, and emotional value. For example, a selection of images having subjects that are known and important to a customer makes a photographic product more valuable and appealing to the customer.
In conventional practice, images for a photographic product are selected by a product designer or customer, either manually or with the help of tools. For example, graphic and imaging software tools are available to assist a user in laying out a multi-image product, such as a photo-book, with a computer. Similarly, on-line tools available over the internet from a remote computer server enable users to specify photographic products. The Kodak Gallery provides such image-product tools. However, in many cases consumers have a large number of images, for example stored in an electronic album in a computer-controlled electronic storage device using desktop or on-line imaging software tools. The selection of an appropriate variety of images from the large number of images available can be tedious and time consuming.
A variety of methods are known in the prior art for automating the selection of images from an image collection, for example as described in U.S. Patent Application Publication 2011/0123124. It is known to select images based on an image quality criterion, to select images shared in a social network that are the most commented upon or that have a relatively large number of approvals, and to recognize individuals depicted in a photograph. Semantic understanding of an image can be used to automatically create an image product, as described in WO 2008/156558 and U.S. Pat. No. 7,035,467.
U.S. Patent Application Publication 2007/0177805 describes a method of searching through a collection of images, includes providing a list of individuals of interest and features associated with such individuals; detecting people in the image collection; determining the likelihood for each listed individual of appearing in each image collection in response to the people detected and the features associated with the listed individuals; and selecting in response to the determined likelihoods a number of images such that each individual from the list appears in the selected images.
U.S. Pat. No. 6,671,405 discloses an approach which computes a metric of “emphasis and appeal” of an image, without user intervention. A first metric is based upon a number of factors, which can include: image semantic content (e.g. people, faces); objective features (e.g., colorfulness and sharpness); and main subject features (e.g., size of the main subject). A second metric compares the factors relative to other images in a collection. The factors are integrated using a trained reasoning engine. The method described in U.S. Patent Application Publication 2004/0075743 by Chantani et al., is somewhat similar and discloses the sorting of images based upon user-selected parameters of semantic content or objective features in the images. U.S. Pat. No. 6,816,847 to Toyama, discloses an approach to compute the aesthetic quality of images through the use of a trained and automated classifier based on features of the image. Recommendations to improve the aesthetic score based on the same features selected by the classifier can be generated with this method. U.S. Patent Publication 2011/0075917 describes estimating aesthetic quality of digital images.
These various methods assist in automating the selection of images in a collection based on a variety of criteria but do not include automatically selecting images based on their emotional appeal and value to a specific individual. There is a need therefore, for a method that reduces the effort required by a customer to select images from a collection for a multi-image product and that provides a multi-image product with emotional meaning and value to the customer.